Autonomous Condensing Of Pallets Of Items In A Warehouse

ABSTRACT

Examples described may enable consolidating pallets of items in a warehouse. An example method includes receiving real-time item information including pallet locations in a warehouse and inventory of items arranged on the pallets; based on the real-time item information, identifying a set of pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information and determining, based on the real-time item and robotics information, an amount of time to condense the items on the set of pallets into a single pallet and a quantity of pallets that will become empty as a result of condensing the items; and, based on the amount of time being less than a threshold time and the quantity of pallets exceeding a threshold quantity of pallets, causing robotic devices to condense the items into the single pallet.

BACKGROUND

A warehouse may be used for storage of goods by a variety of differenttypes of commercial entities, including manufacturers, wholesalers, andtransport businesses. Example stored goods may include raw materials,parts or components, packing materials, and finished products. In somecases, the warehouse may be equipped with loading docks to allow goodsto be loaded onto and unloaded from delivery trucks or other types ofvehicles. The warehouse may also use rows of pallet racks to allow forstorages of pallets—namely, flat transport structures that containstacks of items, such as goods contained in boxes. Additionally, thewarehouse may have machines or vehicles for lifting and moving goods orpallets of items, such as cranes and forklifts. Human operators may beemployed to operate machines, vehicles, and other equipment. In somecases, one or more of the machines or vehicles may be robotic devicesguided by computer control systems.

SUMMARY

In one aspect, the present application describes a method. The methodmay involve receiving, at a warehouse control system (WCS), real-timeitem information including real-time locations of pallets positioned ina warehouse and real-time inventory of items arranged on the pallets,where the real-time inventory of the items includes a content of eachpallet. The method may further involve, based on the real-time iteminformation, identifying a set of two or more pallets of which at leastone pallet includes less than a threshold quantity of a type of item.The method may further involve receiving, at the WCS, real-time roboticsinformation. The method may still further involve, based on thereal-time item information and the real-time robotics information,determining an amount of time to condense the items on the set ofpallets into a single pallet by the plurality of robotic devices. Themethod may still further involve, based on the real-time iteminformation and the real-time robotics information, determining aquantity of pallets that will become empty as a result of condensing theitems on the set of pallets into the single pallet. The method may yetstill further involve causing, by the WCS, the plurality of roboticdevices to condense the items on the set of pallets into the singlepallet based on the amount of time being less than a threshold amount oftime and further based on the quantity of pallets that will become emptybeing greater than a threshold quantity of pallets.

In another aspect, the present application describes a system. Thesystem may comprise a plurality of robotic devices in a warehouse, atleast one processor, and data storage comprising instructions executableby the at least one processor to cause the system to perform operations.The operations may include receiving real-time item informationincluding real-time locations of pallets positioned in the warehouse andreal-time inventory of items arranged on the pallets, where thereal-time inventory of the items includes a content of each pallet. Theoperations may further include, based on the real-time item information,identifying a set of two or more pallets of which at least one palletincludes less than a threshold quantity of a type of item. Theoperations may further include receiving real-time robotics information.The operations may still further include, based on the real-time iteminformation and the real-time robotics information, determining anamount of time to condense the items on the set of pallets into a singlepallet by the plurality of robotic devices. The operations may stillfurther include, based on the real-time item information and thereal-time robotics information, determining a quantity of pallets thatwill become empty as a result of condensing the items on the set ofpallets into the single pallet. The operations may yet still furtherinclude causing one or more of the plurality of robotic devices tocondense the items on the set of pallets into the single pallet based onthe amount of time being less than a threshold amount of time andfurther based on the quantity of pallets that will become empty beinggreater than a threshold quantity of pallets.

In still another aspect, the present application describes anon-transitory computer-readable medium having stored thereon programinstructions that when executed by a computing system that includes atleast one processor cause the computing system to perform operations.The operations may include receiving real-time item informationincluding real-time locations of pallets positioned in a warehouse andreal-time inventory of items arranged on the pallets, where thereal-time inventory of the items includes a content of each pallet. Theoperations may further include, based on the real-time item information,identifying a set of two or more pallets of which at least one palletincludes less than a threshold quantity of a type of item. Theoperations may further include receiving real-time robotics information.The operations may still further include, based on the real-time iteminformation and the real-time robotics information, determining anamount of time to condense the items on the set of pallets into a singlepallet by the plurality of robotic devices. The operations may stillfurther include, based on the real-time item information and thereal-time robotics information, determining a quantity of pallets thatwill become empty as a result of condensing the items on the set ofpallets into the single pallet. The operations may yet still furtherinclude causing one or more of the plurality of robotic devices tocondense the items on the set of pallets into the single pallet based onthe amount of time being less than a threshold amount of time andfurther based on the quantity of pallets that will become empty beinggreater than a threshold quantity of pallets.

In yet another aspect, a system is provided that includes a means forreceiving real-time item information including real-time locations ofpallets positioned in a warehouse and real-time inventory of itemsarranged on the pallets, where the real-time inventory of the itemsincludes a content of each pallet. The system may further include ameans for, based on the real-time item information, identifying a set oftwo or more pallets of which at least one pallet includes less than athreshold quantity of a type of item. The system may further include ameans for receiving real-time robotics information. The system may stillfurther include a means for, based on the real-time item information andthe real-time robotics information, determining an amount of time tocondense the items on the set of pallets into a single pallet by theplurality of robotic devices. The system may still further include ameans for, based on the real-time item information and the real-timerobotics information, determining a quantity of pallets that will becomeempty as a result of condensing the items on the set of pallets into thesingle pallet. The system may yet still further include a means forcausing one or more of the plurality of robotic devices to condense theitems on the set of pallets into the single pallet based on the amountof time being less than a threshold amount of time and further based onthe quantity of pallets that will become empty being greater than athreshold quantity of pallets.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects,implementations, and features described above, further aspects,implementations, and features will become apparent by reference to thefigures and the following detailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an example warehouse, in accordance with at least someimplementations described herein.

FIG. 2 is a simplified block diagram illustrating components of anexample computing system, in accordance with at least someimplementations described herein.

FIG. 3 is a flow chart of an example method, in accordance with at leastsome implementations described herein.

FIGS. 4A, 4B, and 4C illustrate example operation of robotic devices ina warehouse, in accordance with at least some implementations describedherein.

DETAILED DESCRIPTION

Example methods and systems are described herein. It should beunderstood that the words “example,” “exemplary,” and “illustrative” areused herein to mean “serving as an example, instance, or illustration.”Any implementation or feature described herein as being an “example,”being “exemplary,” or being “illustrative” is not necessarily to beconstrued as preferred or advantageous over other implementations orfeatures. The example implementations described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated herein. Additionally, in this disclosure, unlessotherwise specified and/or unless the particular context clearlydictates otherwise, the terms “a” or “an” means at least one, and theterm “the” means the at least one.

As used herein, the term “warehouse” may refer to any physicalenvironment in which items or pallets of items may be manipulated,processed, and/or stored by robotic devices. In some examples, awarehouse may be a single physical building or structures. In otherexamples, some fixed components may be installed or otherwise positionedwithin the environment before or during object processing. In additionalexamples, a warehouse may include multiple separate physical structures,and/or may also include physical spaces that are not covered by aphysical structure as well.

An example warehouse may include a homogeneous or heterogeneous group ofrobotic devices and a control system configured to manage the roboticdevices. This group of robotic devices can exist on their own withouthuman operators in an autonomous warehouse environment, or can beco-located with human operators (e.g., human-driven devices such asforklifts) in a collaborative warehouse environment. In the context of awarehouse, such a control system can be referred to as a warehousecontrol system (WCS). The warehouse may also include a variety of items(e.g., products) arranged on pallets, and the pallets may be arranged invarious positions in the warehouse. For instance, pallets may bepositioned directly on the floor of the warehouse, stacked on otherpallets, placed in pallet racks, and/or stored in shipping containers.When each pallet arrives at the warehouse, the WCS may utilize allavailable information to determine the most appropriate location tostore the pallet.

A group of robotic devices may be used in a warehouse setting for anumber of different applications. One possible application includesorder fulfillment (e.g., for individual customers), in which cases maybe opened and individual items from the cases may be put into packagingwithin boxes to fulfill individual orders. Another possible applicationincludes distribution (e.g., to stores or other warehouses), in whichmixed pallets may be constructed containing groups of different types ofitems (i.e., types of products) to ship to stores. A further possibleapplication includes cross-docking, which may involve transportingbetween shipping containers without storing anything (e.g., items may bemoved from four 40-foot trailers and loaded into three lighter tractortrailers, and could also be palletized). Numerous other applications arealso possible.

As a general matter, the day-to-day operations in the warehouse, such asoperations regarding pallets of items, may depend on various factors.Such factors may include a history of activities in the warehouse, ahistory of pallet locations, a history of demand for certain items,trends of items received at the warehouse, trends of items shipped outof the warehouse, received orders for items, forecasted activities inthe warehouse, forecasted demand for certain items, forecasted items tobe received at the warehouse, forecasted items to be shipped out of thewarehouse, business goals (e.g., promotional offers such as same-daydelivery), and human and/or robotic resource availability to arrange thepallets, among many other possibilities. The WCS may be configured touse these and other factors with machine learning to facilitate improvedmanagement of the warehouse.

Provided herein are example methods and systems for autonomouslyconsolidating—or, “condensing”—pallets of items in the warehouse. Inaccordance with an example method, the WCS may receive, determine, orotherwise access real-time item information including real-timelocations of pallets of items in the warehouse, a real-time inventory ofitems in the warehouse, and a real-time content of each pallet (e.g.,which items are on which pallets). The WCS may use this informationdiscussed above to identify a set of two or more pallets in thewarehouse of which at least one pallet includes less than a thresholdquantity of items. In this manner, the WCS can efficiently locatepallets that do not have many items on them but are taking up space inthe warehouse, and then select them as candidates for condensing. By wayof example, the WCS may locate (i) a first pallet that has only twotelevision boxes on it, (ii) a second pallet that has one television boxon it, and (iii) a third pallet that has six television boxes on it. Butby comparison, in this example, the average pallet in the warehouse(among all pallets in the warehouse, or among a smaller group of palletshaving distinct characteristics) may be configured to carry up to tenboxes of a television product (e.g., a 55-inch LED television set). Inother words, the average pallet may have a capacity of ten. Byconsolidating these three pallets, a single pallet of nine televisionboxes would result, thus eliminating two pallets in the warehouse.

As a general matter, in determining whether, when, and how to condensethe set of pallets the WCS may make various considerations. As anexample, the WCS may determine what resources would be needed tocondense the identified set of pallets, such as the time it would takefor robotic devices to condense the set of pallets. If the WCSdetermines that resources meet certain criteria, the WCS may theninstruct the robotic devices to condense the set of pallets. If not, theWCS may not instruct the robotic devices the set of pallets until theWCS determines that the resources meet certain criteria. As anotherexample, the WCS may not instruct the robotic devices to condense theset of pallets unless a certain threshold quantity of pallets willbecome empty as a result of the condensing, thereby making more room inthe warehouse for pallets of items that are more full.

To facilitate the WCS's determination of available robotic resources,the WCS may receive, determine, or otherwise have access to real-timerobotics information relating to robot activities in the warehouse. Inparticular, the real-time robotics information may include real-timelocations of the robotic devices, real-time task progress updates forrobotic tasks that are in-progress and/or to-be-completed, a taskschedule, and respective fixed measurements of how much time the roboticdevices will take to perform various tasks, among other possibleinformation. The WCS may then use at least the real-time roboticsinformation to determine how long it would take the robotic devices tocondense the set of pallets. If the amount of time is less than athreshold amount of time, the WCS may instruct the robotic devices tocondense the set of pallets.

Dynamically and autonomously condensing pallets in the warehouse mayprovide various industrial and business advantages. For example, the useof the WCS and robotic devices may greatly reduce or eliminate the needfor human labor in condensing pallets in the warehouse. As anotherexample, as demand for items changes over time, the WCS may choose tocondense (or not condense) sets of pallets to best reflect the currentdemand at various points in time, or may proactively change the layoutof pallets to best reflect forecasted demand at a future point in time.For instance, when items are not selling very well, it may be desirableto condense pallets of those items to make more room for items that areselling well. As yet another example, as available space in thewarehouse increases and decreases, the WCS may balance a considerationof available space with other considerations, such as demand, ease ofaccess to items, etc., in order to determine which pallets should becondensed to best utilize the available space in the warehouse in lightof those considerations. Other examples are possible as well.

It should be noted that in alternate implementations, one or morecomputing entities associated with the warehouse, such as the WCS and/orother computing systems, may gather, update, process, and/or provide forreceipt by the WCS the item information and/or the robotics informationless frequently than in real-time.

Reference will now be made in detail to various implementations,examples of which are illustrated in the accompanying drawings. In thefollowing detailed description, numerous specific details are set forthin order to provide a thorough understanding of the present disclosureand the described implementations. However, the present disclosure maybe practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theimplementations.

Referring now to the figures, FIG. 1 depicts an example warehouse 100 inaccordance with at least some implementations described herein. Thewarehouse 100 includes various types of robotic devices that may becontrolled to collaborate to perform tasks related to the processing ofitems, pallets, etc. within the warehouse. Certain example types andnumbers of different robotic devices are shown here for illustrationpurposes, but the warehouse may employ more or fewer robotic devices,may omit certain types shown here, and may also include other types ofrobotic devices not explicitly shown.

One example type of robotic device shown in FIG. 1 is an autonomous forktruck 102, a mobile device with a forklift that may be used to transportpallets of boxes and/or to lift pallets of boxes (e.g., to place thepallets onto a rack for storage). Another example type of robotic deviceshown is an autonomous guided vehicle (AGV) 104, which may be arelatively small, mobile device with wheels that may function totransport individual items or other objects from one location to anotherwithin the warehouse. An additional example type of robotic device is arobotic truck loader/unloader 106, a mobile device with a roboticmanipulator as well as other components such as sensors to facilitateloading and/or unloading items and/or pallets onto and/or off of trucksor other vehicles. For instance, robotic truck unloader 106 may be usedto load pallets or individual items onto delivery truck 108, which islocated at a shipping dock 110 of the warehouse. In some examples,movements of delivery truck 108 (e.g., to deliver packages to anotherwarehouse) may also be coordinated with robotic devices in thewarehouse.

Other types of mobile devices than those illustrated here may also beincluded as well or instead. In some examples, one or more roboticdevices may use different modes of transportation besides wheels on theground. For instance, one or more robotic devices may be airborne (e.g.,quadcopters), and may be used for tasks such as moving objects orcollecting sensor data of the environment.

Any of the robotic devices described herein may include one or moresensor(s) such as force sensors, proximity sensors, load sensors,position sensors, touch sensors, depth sensors, ultrasonic rangesensors, infrared sensors, Global Positioning System (GPS) receivers,sonar, optical sensors, biosensors, Radio Frequency identification(RFID) sensors, Near Field Communication (NFC) sensors, wirelesssensors, compasses, smoke sensors, light sensors, audio sensors,microphones, speakers, radar, cameras (e.g., color cameras, grayscalecameras, and/or infrared cameras), depth sensors (e.g., Red Green Blueplus Depth (RGB-D), lasers, a light detection and ranging (LIDAR)device, a structured-light scanner, and/or a time-of-flight camera), astereo camera, motion sensors (e.g., gyroscope, accelerometer, inertialmeasurement unit (IMU), and/or foot step or wheel odometry), and/orrange sensors (e.g., ultrasonic and/or infrared), among others. Thesensor(s) may provide sensor data to a processor(s) to allow forappropriate interaction of a robotic device with the environment.Additionally, a robotic device may also include one or more powersource(s) configured to supply power to various components of therobotic device. Any type of power source may be used such as, forexample, a gasoline engine or a battery.

In further examples, the warehouse 100 may also include various fixedcomponents. One or more fixed robotic devices may be used to move orotherwise process items. For instance, a pedestal robot 112 may includea robotic arm elevated on a pedestal that is fixed to the ground floorwithin the warehouse. The pedestal robot 112 may be controlled todistribute items between other robots and/or to stack and unstackpallets of items. For example, the pedestal robot 112 may pick up andmove boxes from nearby pallets and distribute the items to individualAGV's 104 for transportation to other locations within the warehouse.

In further examples, the warehouse 100 may include additional fixedcomponents, such as storage racks (not shown) that may be used to storepallets and/or other objects within the warehouse. Such storage racksmay be designed and positioned to facilitate interaction with a roboticdevice, such as autonomous fork truck 102. In addition to or alternativeto storage racks, certain ground space in the warehouse 100 may beselected and used for storage of pallets. For instance, some pallets maybe positioned within the warehouse environment at chosen locations forcertain periods of time to allow the pallets to be picked up,distributed, or otherwise processed by a robotic device. Any fixedcomponent in warehouse 100 may be equipped with one or more sensors, asdescribed herein.

In some examples, any or all of the robotic devices in the warehouse 100may include one or more sensors, one or more computers, and one or morerobotic arms. A sensor may be used to perform various operations, suchas scanning areas within the warehouse 100 in order to capture visualdata and/or three-dimensional (3D) depth information. Data from a scanmay then be integrated into a representation of larger areas in order toprovide digital environment reconstruction. In additional examples, thereconstructed environment may then be used for identifying items,pallets, or other objects to pick up, determining pick positions foritems or pallets, and/or planning collision-free trajectories alongwhich robotic devices should travel. The warehouse 100 may also includesensors such as cameras or other types of sensors that are not connectedto robotic devices. For instance, various sensors may be affixed tovarious locations in the warehouse, such as the walls, ceilings, etc.

As shown in FIG. 1, the warehouse 100 includes a variety of pallets,each of which may contain one type of item or may contain multiple typesof items. In particular, the warehouse 100 includes rows 114, 116, 118,120, 122, and 124, each of which includes a line of pallets that containthe same type of item. For instance, the pallets of row 114, row 118,and row 124 may each include a first type of item, the pallets of row116 and row 122 may each include a second type of item, and the palletsof row 120 may each include a third type of item. Alternatively, withinany of the rows, adjacent pallets may have different types of items fromone another. As a general matter, any two adjacent pallets in thewarehouse—including adjacent pallets in the same row or adjacent palletsin two adjacent rows—may or may not have the same type of item.

The pallets in rows 114, 116, 118, 120, 122, and 124 may be considered“full” pallets—namely, pallets that contain the maximum quantity ofitems allotted per pallet. The maximum quantity of items allotted perpallet may be the same or different for multiple pallets, and may be aquantity defined by the WCS or by a human. Further, the maximum quantityof items allotted per pallet—or, “capacity”—may depend on variousfactors, such as pallet structure (e.g., the maximum weight that apallet can support) and the size and weight of the items on the pallet.Still further, the capacity may be the same across all pallets in thewarehouse or may be different for respective groups of pallets. Forinstance, pallets for 55-inch televisions may have a smaller capacitythan pallets for spark plugs since boxes for televisions are likely tobe larger than boxes for spark plugs. In practice, the capacity for agiven pallet or group of pallets may be determined in various ways. TheWCS may itself determine what the capacity should be or may receive suchcapacity information from another computing entity or from manual inputfrom a human operator.

By comparison, the warehouse 100 may also include various pallets thatare less than full, such as pallet 126, pallet 128, pallet 130, andpallet 132. Each of these pallets may include the same type of item ormay include different types of items.

In some examples, the warehouse 100 may additionally include a dedicatedregion for performing certain tasks, such as a pallet condensing station134, which is shown to be next to shipping dock 110. In an examplescenario, the WCS may instruct autonomous fork truck 102 and/or otherrobotic devices truck to retrieve two or more pallets that are not atcapacity and bring them to pallet condensing station 134 to condense thepallets into a single pallet. To facilitate this, pallet condensingstation 134 may include empty pallets 136 on which to place thecondensed items. In other implementations, a pallet condensing stationmay take other forms and/or be located in another area or areas ofwarehouse 100.

In further examples, the warehouse 100 may additionally include batteryexchange/charging stations (not shown). Within examples, some or all ofthe mobile robotic devices such as autonomous fork truck 102 or AGV 104may periodically receive charged batteries from a battery exchangestation equipped with multiple battery chargers. In particular, thebattery exchange station may replace a robotic device's old batterieswith recharged batteries, which may prevent robotic devices from havingto sit and wait for batteries to charge. The battery exchange stationmay be equipped with a robotic manipulator such as a robotic arm. Therobotic manipulator may remove batteries from an individual mobilerobotic device and attach the batteries to available battery chargers.The robotic manipulator may then move charged batteries located at thebattery exchange station into the mobile robotic device to replace theremoved batteries. For instance, an AGV with a weak battery may becontrolled to move over to a battery exchange station where a roboticarm pulls a battery out from the AGV, puts the battery in a charger, andgives the AGV a fresh battery.

Within examples, one or more robotic devices may be brought into thewarehouse 100 to create a map of the warehouse 100 space beforedetermining placement of other objects, such as any fixed roboticdevices or components discussed above, as well as any pallets. Herein, a“map” refers to information representative of a positioning of elementsin a particular area of an environment, and/or representative of arelationship of certain elements to other elements or to theenvironment. Within example implementations, a map is a digital map,determined by collecting and compiling data representative ofrelationships between elements in the given environment, and thenformatting such data into a virtual form, such as a virtual 2D or 3Dimage. A map can be a real-time or non-real-time representation of theelements and environment at issue, detailing such elements, theenvironment, and/or points of interest on such elements and/or in theenvironment. Additionally or alternatively, at least a portion of a mapcan be generated through non-robotic means, using traditional or digitalsurveying techniques, or using other techniques.

Once map information is available, a computing system such as the WCSmay determine (e.g., by running simulations) how to layout suchcomponents within the space available. In certain cases, a layout may bechosen to minimize the amount of space taken up by the objects. Palletscan be placed at predetermined locations, or can be placed at random, orcan be intelligently positioned by the WCS based on consideration ofvarious factors, including factors discussed herein.

To coordinate actions of various devices within the warehouse 100 (e.g.,the robotic devices, and possibly other components such as a batterycharging station, remotely-controlled shipping dock doors,remotely-controlled ramps, etc.), a global control system, such as aremote, cloud-based server system, may communicate (e.g., throughwireless communication) with some or all of the components and/or withseparate local control systems of individual components. Any computingsystem described herein, such as a WCS, may take the form of such aglobal control system.

FIG. 2 is a simplified block diagram illustrating components of anexample computing system 200, in accordance with at least someimplementations described herein. For instance, computing system 200 canserve as a WCS and control aspects of a warehouse such as warehouse 100,including dynamically controlling operations of robotics devices orother components in the warehouse, as noted above. As such, computingsystem 200 can include various components, such as processor 202, datastorage unit 204, communication interface 206, and/or user interface208. The components of computing system 200 can be connected to eachother (or to another device, system, or other entity) via connectionmechanism 210. In this disclosure, the term “connection mechanism” meansa mechanism that facilitates communication between two or more devices,systems, or other entities. For instance, a connection mechanism can bea simple mechanism, such as a cable or system bus, or a relativelycomplex mechanism, such as a packet-based communication network (e.g.,the Internet). In some instances, a connection mechanism can include anon-tangible medium (e.g., where the connection is wireless). Computingsystem 200 can include more or less components in other exampleimplementations.

Processor 202 may take the form of a general-purpose processor (e.g., amicroprocessor) and/or a special-purpose processor (e.g., a digitalsignal processor (DSP)). In some instances, computing system 200 mayinclude a combination of processors.

Data storage unit 204 may include one or more volatile, non-volatile,removable, and/or non-removable storage components, such as magnetic,optical, or flash storage, and/or can be integrated in whole or in partwith processor 202. As such, data storage unit 204 may take the form ofa non-transitory computer-readable storage medium, having stored thereonprogram instructions (e.g., compiled or non-compiled program logicand/or machine code) that, when executed by processor 202, causecomputing system 200 to perform one or more acts and/or functions, suchas those described in this disclosure. Computing system 200 can beconfigured to perform one or more acts and/or functions, such as thosedescribed in this disclosure. Such program instructions can defineand/or be part of a discrete software application. In some instances,computing system 200 can execute program instructions in response toreceiving an input, such as from communication interface 206 and/or userinterface 208. Data storage unit 204 may also store other types of data,such as those types described in this disclosure.

Communication interface 206 can allow computing system 200 to connect toand/or communicate with another other entity according to one or moreprotocols. In an example, communication interface 206 can be a wiredinterface, such as an Ethernet interface or a high-definitionserial-digital-interface (HD-SDI). In another example, communicationinterface 206 can be a wireless interface, such as a cellular or Wi-Fiinterface. A connection can be a direct connection or an indirectconnection, the latter being a connection that passes through and/ortraverses one or more entities, such as such as a router, switcher, orother network device. Likewise, a transmission can be a directtransmission or an indirect transmission.

User interface 208 can facilitate interaction between computing system200 and a user of computing system 200, if applicable. As such, userinterface 208 can include input components such as a keyboard, a keypad,a mouse, a touch-sensitive panel, a microphone, and/or a camera, and/oroutput components such as a display device (which, for example, can becombined with a touch-sensitive panel), a sound speaker, and/or a hapticfeedback system. More generally, user interface 208 can include hardwareand/or software components that facilitate interaction between computingsystem 200 and the user of the computing device system.

As indicated above, computing system 200 may be configured todynamically control aspects of a warehouse, such as by coordinatingoperation of robotic devices operating in the warehouse. As an example,computing system 200 may determine for a given robotic device a schedulethat indicates a series of tasks for the robotic device to performthroughout an entire period of time (e.g., 12 hours) and adjust theschedule using subsequent information received from the robotic device(e.g., sensor data, task progress updates), other robotic devices,and/or other systems (e.g., a sensor system) positioned in thewarehouse. As another example, computing system 200 may be configured tocoordinate operations for multiple robotic devices, including aligningschedules so that two or more robotic devices are located in the samearea during a period of time, perhaps in case the computing system haspredicted that the two or more robotic devices may be needed to assistwith another task in that area during that time. As yet another example,computing system 200 may also arrange schedules so as to not have athreshold high amount of robotic devices performing tasks in the samearea during the same period of time, thereby avoiding generation ofexcess traffic in the warehouse. As yet another example, computingsystem 200 may alter the schedules of one or more robotic devices tosave resources, such as by cancelling a first robotic device's scheduledpickup of a pallet when a second robotic device is closer in distance tothe pallet than the first robotic device, and instead instructing thesecond robotic device to pick up the pallet.

In some implementations, computing system 200 may be integrated with awarehouse management system (WMS). Through this integration, computingsystem 200 may access various types of information maintained by theWMS, such as warehouse inventory information, demand/order information,expected inbound receiving/delivery information, expected outboundshipping information, and/or other types of information discussedherein. In other implementations, computing system 200 itself mayfunction as a WMS. As such, computing system 200 may gather and maintainthis and/or other information.

In some examples, computing system 200 may function as or include acentral planning system that assigns tasks to different robotic devices.Herein, a “task” refers to an operation that is assigned to at least oneentity for that entity or entities to perform. Within exampleimplementations, such a task is assigned to the at least one entity by asystem that monitors, governs, or otherwise manages the at least oneentity in order to facilitate a performance of the task by the at leastone entity.

The central planning system may employ various scheduling algorithms todetermine which devices will complete which tasks at which times. Forinstance, an auction type system may be used in which individual robotsbid on different tasks, and the central planning system may assign tasksto robots to minimize overall costs. In additional examples, the centralplanning system may optimize across one or more different resources,such as time, space, or energy utilization. In further examples, aplanning or scheduling system may also incorporate aspects of thegeometry and physics of box picking, packing, or storing.

Planning control may also be distributed across individual systemcomponents. For example, computing system 200 may issue instructionsaccording to a global system plan, and individual system components mayalso operate according to separate local plans. Additionally, differentlevels of detail may be included within a global plan, with otheraspects left for individual robotic devices to plan locally. Forinstance, mobile robotic devices may be assigned target destinations bya global planner but the full routes to reach those target destinationsmay be planned or modified locally.

To facilitate planning control, computing system 200 may employ varioustechniques to monitor the locations of robotic devices in the warehouse100. These locations may be real-time locations or non-real-timelocations. To further facilitate planning control, in someimplementations, robotic devices may be configured to continuously orperiodically “publish” (e.g., transmit) their locations or the locationsof other robotic devices to computing system 200 so that computingsystem 200 can in turn update the locations of the robotic devices.Computing system 200 and/or the robotic devices can employ othertechniques as well to facilitate monitoring locations of the roboticdevices.

In additional examples, computing system 200 may employ varioustechniques to monitor the real-time inventory of the pallets in thewarehouse 100 and/or the real-time locations of the pallets. Forinstance, computing system 200 may instruct one or more mobile roboticdevices with vision systems to travel the warehouse and identify thelocation and content of each pallet. Such information may be gathered byway of scanning barcodes or other labels on the individual pallets orindividual items, as discussed below, or in other manners. Further,while a robotic device is transporting a pallet and repeatedly publishesits own location in real-time during transportation of the pallet, theWCS may interpret the real-time location of the robotic device to be thelocation of the pallet as well. Still further, after a robotic devicedrops off the pallet somewhere in the warehouse, the robotic device maynotify the WCS of the new location of the pallet. Computing system 200and/or the robotic devices can employ other techniques as well tofacilitate monitoring locations and contents of the pallets.

In additional examples, a central planning system may be used inconjunction with local vision systems on individual robotic devices tocoordinate functions of various robotic devices. For instance, a centralplanning system may be used to get robotic devices at least relativelyclose to where they need to go (e.g., within millimeters orcentimeters). However, it may be difficult for the central planningsystem to command robots with millimeter precision, unless the roboticdevices are bolted to rails or other measured components are used toprecisely control robot positions. Local vision systems and planning forindividual robotic devices may therefore be used to allow for elasticitybetween different robotic devices. A general planner may be used to geta robotic device close to a target location, at which point local visionsystem of the robotic device may take over. In some examples, mostrobotic functions may be position-controlled to get the robotsrelatively close to target locations, and then vision systems andhandshakes may be used when needed for local control.

A visual handshakes may enable two robotic devices to identify oneanother by quick response (QR) tag or other characteristics, and toperform collaborative operations within the warehouse 100. In additionalexamples, an item may be provided with a visual tag as well or instead.A visual tag may be used by a robotic device to perform an operation onthe item using a local vision system. In particular, a tag may be usedto facilitate manipulation of an item by a robotic device. For instance,a tag on a location on a pallet may be used to inform a forklift whereor how to lift up the pallet.

Within additional examples, robotic devices may use their local visionsystems to scan and identify individual items or pallets of items duringtasks in which robotic devices need to manipulate the items/pallets insome manner. To facilitate this in part, for instance, a givenitem/pallet may include a machine-readable code (e.g., QR code orbarcode) having encoded information about the given item/pallet. As aresult of the scanning, the machine-readable code may provide a roboticdevice's local computing system and/or computing system 200 with theinformation about the given item/pallet. For instance, for a pallet,such information may include what type of item the pallet carries.Further, such information may include a history of the pallet, such as(i) where in the warehouse the pallet has been, (ii) how many times thepallet has been moved, (iii) when the pallet was moved, (iv) indicationsof damage to the pallet, if any, and (v) whether the pallet is markedfor delivery to another area of the warehouse (e.g., a shipping dock, orfor storage in another area), among other types of information. A givenitem/pallet may additionally or alternatively include a label or othersource of such information that the robotic device can scan to identifythe item/pallet and obtain such information.

Scanning items/pallets in this manner may have various advantages, suchas locating and keeping track of items/pallets as they are moved into,out of, and around the warehouse 100. Further, a potential benefit ofsuch scanning is added transparency, both on the supplier side and theconsumer side. On the supplier side, for example, information aboutcurrent locations of inventory may be used to avoid overstocking and/orto move items/pallets to different locations or warehouses to anticipatedemand. On the consumer side, for example, the information about currentlocations of items may be used to determine when an item/pallet will bedelivered with improved accuracy.

Within additional examples, computing system 200 may over time optimizedeployment and/or planning strategies for fixed and/or mobilecomponents. For instance, computing system 200 (e.g., a cloud-basedserver system) may incorporate data and information from individualrobotic devices within the warehouse and/or from external sources.Strategies may then be refined over time to enable the robotic devicesto use less space, less time, less power, less electricity, or tooptimize across other variables. In some examples, optimizations mayspan across multiple warehouses, possibly including other warehouseswith other robotic devices and/or traditional warehouses without suchrobotic devices. For instance, computing system 200 may incorporateinformation about delivery vehicles and transit times between facilitiesinto central planning.

Similarly, computing system 200 may over time optimize planningstrategies to meet existing demand for items (e.g., current orders forproducts), meet forecasted demand for items, meet certain businessgoals, etc.

In some examples, a central planning system may sometimes fail, such aswhen a robotic device gets stuck or when items get dropped in a locationand lost. Local vision systems may also therefore provide robustness byinserting redundancy to handle cases where the central planner fails.For instance, as an automated pallet jack passes and identifies an item,the pallet jack may send information up to a remote, cloud-based serversystem. Such information may be used to fix errors in central planning,help to localize robotic devices, or to identify lost items.

In further examples, computing system 200 may dynamically update a mapof the warehouse 100 and objects undergoing processing by the roboticdevices. In some examples, the map may be continuously updated withinformation about dynamic objects (e.g., moving robotic devices anditems/pallets moved by robotic devices). In additional examples, adynamic map could contain information on both the current arrangement ofobjects within a warehouse (or across multiple warehouses) as well asinformation about what is anticipated in the future, such as in the nearterm (e.g., in upcoming seconds, minutes, hours, or even days) or longterm (e.g., in upcoming weeks, months, or even years). For instance, themap could show current locations of moving robotic devices andanticipated locations of the robotic devices in the future, which may beused to coordinate activity between robotic devices. The map could alsoshow current locations of items undergoing processing as well asanticipated future locations of the items (e.g., where an item is nowand when the item is anticipated to be shipped out). Further, the mapcould show anticipated locations of items that have yet to arrive at thewarehouse. For instance, computing system 200 may consider a history oforders for items, forecasted orders, a history of pallet relocation,and/or other known or predicted information to cause the map to showwhere pallets of items should be placed in the warehouse when theyarrive.

In line with the discussion above, computing system 200 may alsoschedule battery exchanges. For instance, individual mobile roboticdevices may be configured to monitor their battery charge status. Therobotic devices may send information to computing system 200 indicatingthe status of their batteries. This information may then be used bycomputing system 200 to schedule battery replacements for individualrobotic devices when needed or convenient.

FIG. 3 is a flow chart of an example method, in accordance with at leastsome implementations described herein. The method shown in FIG. 3presents an implementation of a method that, for example, could be usedwith the systems shown in FIGS. 1 and 2, for example, or may beperformed by a combination of any components of those Figures. Inaddition, such an implementation of a method could be carried out inaccordance with the aspects illustrated in FIGS. 4A, 4B, and 4C. Themethod may include one or more operations, or actions as illustrated byone or more of blocks 300-310. Although the blocks are illustrated in asequential order, these blocks may in some instances be performed inparallel, and/or in a different order than those described herein. Also,the various blocks may be combined into fewer blocks, divided intoadditional blocks, and/or removed based upon the desired implementation.

Furthermore, for the method and other processes and methods disclosedherein, the flowchart shows operation of one possible implementation. Inthis regard, each block may represent a module, a segment, or a portionof program code, which includes one or more instructions executable byone or more processors for implementing specific logical operations orsteps in the process. The program code may be stored on any type ofcomputer readable medium, for example, such as a storage deviceincluding a disk or hard drive. The computer readable medium may includea non-transitory computer readable medium, for example, such ascomputer-readable media that stores data for short periods of time likeregister memory, processor cache and Random Access Memory (RAM). Thecomputer readable medium may also include non-transitory media, such assecondary or persistent long term storage, like read only memory (ROM),optical or magnetic disks, compact-disc read only memory (CD-ROM), forexample. The computer readable media may also be any other volatile ornon-volatile storage systems. The computer readable medium may beconsidered a computer readable storage medium, a tangible storagedevice, or other article of manufacture, for example.

In addition, for the method and other processes and methods disclosedherein, each block in FIG. 3 may represent circuitry that is wired toperform the specific logical operations in the process.

Operations of the method, and operations of other methods and processesdisclosed herein, are described as performed by a WCS in some examples,such as computing system 200. However, these operations may be performedin whole or in part by other entities or combinations of entities. Forexample, these operations may be managed by a central server, which candistribute operations to smaller peer-to-peer networks or servers thatmay manage portions of the robotic devices.

Within examples, some or all of the operations of this method or othermethods may be performed in response to various trigger events. Forexample, the WCS may determine that a present or future storage capacityrequirement is not met, such as an amount of space taken up in thewarehouse by the pallets exceeding a threshold amount of space, and theWCS may responsively then take certain actions to condense pallets inthe warehouse to reduce the space taken up by the pallets. As anotherexample, the WCS may take certain actions to condense pallets of itemsif an outbound throughput of items does not meet certain throughputrequirements.

As a general matter, there may be various viable strategies for palletcondensing that might lead to different outcomes, such as consolidatingtwo partial pallets to one partial pallet, consolidating two partialpallets to one full pallet, consolidating two partial pallets to a fullpallet and one partial pallet (e.g., if there is an expectation that afull pallet of an item will be shipped), consolidating two partialpallets having different quantities of an item to two partial palletsboth having the same quantity of the item, etc. Any one or more of theoperations discussed herein may be used to facilitate any one or more ofthese strategies or other strategies for condensing pallets.

Within additional examples, the WCS may have access to variousrecords/histories, such as inventory history, pallet relocation history,receiving records, order fulfillment records, shipping records, and thelike from various points in time, ranging from minutes to weeks ormonths in the past, for instance. The WCS may use such records/historiesto determine the expectations of items to be received at the warehouse,the expectations of items to be shipped out of the warehouse, and/orother possible information for use in accordance with the method. An“expectation” may be a prediction (with some degree of uncertainty) ormay be a certainty. In other words, the WCS may maintain or be providedwith existing information such as receiving records, order fulfillmentrecords, shipping records, etc. that may directly indicate to the WCSwhat items will be received/shipped/etc. in the future, or that the WCSmay use with machine learning as a basis for predicting what items willbe received/shipped/etc. in the future. For example, if the WCS hasaccess to orders received for an item that is due to be shipped out in aday, the expectation for that item is that the item will be shipped outin a day. By contrast, for example, if the WCS has access to recordsindicating that many recent orders for an item have come in, or hasaccess to any other information indicating that the item may be insomewhat high demand, the WCS may predict, and thereby expect, that acertain quantity of the item will be arriving at the warehouse and/orshipped out of the warehouse sometime within the upcoming week.

The WCS may use any or all of the various records/histories anddetermined expectations discussed above to facilitate any one or more ofthe operations discussed herein, including at least a portion of themethod discussed with regard to FIG. 3.

There may be scenarios in which the WCS needs to perform multiplecondensing tasks to achieve certain goals, such as freeing up a largeamount of storage space in the warehouse. Accordingly, references to theact of identifying and condensing “a set of pallets” may involve theidentifying and condensing one large set of pallets (e.g., twenty,fifty, or one hundred pallets) or multiple sets of pallets (e.g.,multiple sets of pallets having the same item or multiple sets ofpallets having different items). Alternatively, identified/condensedsets of pallets may be smaller (e.g., two, five, or ten pallets).

At block 300, the method involves receiving real-time item informationincluding real-time locations of pallets positioned in a warehouse andreal-time inventory of items arranged on the pallets, where thereal-time inventory of the items includes a content of each pallet.

The real-time inventory of the items may specify the content of thepallets with varying granularity. For example, the real-time inventorymay specify, for each pallet, which type (or types, if there aremultiple types) of item is arranged on the pallet, and how many of thetype(s) of items are on the pallet (e.g., nine items of type A on palletX, twelve items of type B on pallet Y). Additionally or alternatively,the real-time inventory may specify relationships between items, such aswhether two or more items are complementary. Examples of items that arecomplementary may include DVD players and DVDs, a printer and inkcartridges, or computer hardware and computer software, among otherpossibilities. Complementary parts may be deemed “complementary” tovarious degrees. For instance, in some cases, car batteries and brakepads may be deemed complementary, but in other cases they may not (butin those other cases, car batteries and car spark plugs may be deemedcomplementary instead). As such, the real-time inventory may specifywhether a single pallet includes two or more complementary items (e.g.,a single pallet includes both DVDs and DVD players). Additionally oralternatively, the real-time inventory may specify whether two or morepallets include complementary items (e.g., a first pallet includesprinters and a second pallet includes ink). Other examples are possibleas well.

At block 302, the method involves, based on the real-time iteminformation, identifying a set of two or more pallets of which at leastone pallet includes less than a threshold quantity of a type of item.

Within examples, the WCS may require that, in order for the WCS toinclude a pallet in the identified set, the pallet must be able to becombined with at least one other pallet in the set. In other words, if agiven pallet is at a predetermined capacity, it may not be included inthe set. Within additional examples, the WCS may require that everypallet of the identified set include less than the threshold quantity ofthe item type, rather than just at least one pallet. For instance, theWCS may not include a pallet in the identified set unless that pallethas less than the threshold quantity of the item type. Within additionalexamples, the WCS may include in the identified set only enough palletsto fill up a single pallet without exceeding a predetermined capacity.Further, the WCS may include in the identified set only pallets that,when combined, would be closest to a predetermined capacity. Forinstance, given a capacity of ten, a first pallet with two items, asecond pallet with five items, and a third pallet with five items, theWCS may identify the second and third pallets to combine, but not thefirst pallet. Other examples are possible as well.

Within examples, the threshold quantity may apply on a per item typebasis. For instance, the WCS may not condense pallets of a first type ofitem unless each of those pallets have less than a first thresholdquantity of the first type of item, and may not condense pallets of asecond, different type of item unless each of those pallets have lessthan a second, different threshold quantity of the second type of item.Within additional examples, the threshold quantity may apply acrossmultiple groups of items, where each group includes multiple types ofitems. Within further examples, the threshold quantity may apply to allitems in the warehouse, regardless of type.

Although the description herein primarily focuses on condensing palletsof items of the same type, it should be noted that, in someimplementations, the WCS may identify a set of two or more pallets thatinclude items that are not the same and then take actions to condensethose pallets. By way of example, the WCS may identify a set of two ormore pallets that include less than a threshold quantity ofcomplementary items. For instance, given a threshold quantity of four,the WCS may (i) identify a first pallet that has two of a first type ofitem, (ii) identify a second pallet that has one of a second type ofitem that the real-time inventory indicates as complementary to thefirst type of item, and then (iii) take actions to condense the firstand second pallets into a single pallet.

Additionally or alternatively to the WCS using item type as criteria foridentifying pallets to condense, the WCS may use other criteria. Forexample, the WCS may use shipping expectations to identify and condensepallets that have different types of items, but where the differenttypes of items are all scheduled to be shipped from the warehouse at thesame time (e.g., in the same shipping container, in the same truck,etc.). As another example, the real-time item information may includeexpiration dates of each item and/or lot numbers of each item, and theWCS may use such expiration dates and/or lot numbers to identify andcondense pallets that have the same type of item of item or differenttypes of items. As a more particular example, the act of the WCSidentifying pallets to condense may involve the WCS identifying a set ofpallets of which at least one pallet includes less than a thresholdquantity of a type of item, and that each also include (i) items of thetype that have the same expiration date and/or (ii) items of the typethat have the same lot number.

Further, the WCS's identification of pallets for condensing may beaffected by factors such as business goals and seasonality of items. Forexample, a website may promote same-day delivery for orders of certainitems, and therefore the WCS may target promoted items for condensing,as those items may need to be shipped out faster than other items thatcould be the subject of pallet condensing. As another example, certainitems may be targeted for condensing when they are in-season, and may betargeted over items that are out of season. For instance, in the winter,the WCS may identify pallets of items such as snow boots to condense,but may not identify pallets of items such as swimsuits.

In some implementations, the act of the WCS identifying pallets tocondense into a single pallet may involve the WCS determining multiplecandidate sets of pallets based on any one or more of the criteriadiscussed herein and then selecting one or more of the candidates to bethe pallets to condense. The act of the WCS selecting one or more of thecandidates to be the pallets to condense may be based on the WCScomparing various considerations, such as whichever sets of pallets willtake the least amount of time to for the robotic devices to condense,the availability of robotic devices to perform the condensing, and/orwhether there is an urgent need to condense the pallets. For instance,there may be a scenario where condensing a first candidate set ofpallets better fits the needs for current and/or forecasted receivingand shipment of items at the warehouse than condensing a secondcandidate set of pallets, but takes a longer amount of time to condensethan the second candidate set of pallets. In that scenario, if there isan urgent need to condense pallets and make room in the warehouse, suchas an urgent instruction from a human operator requiring condensing assoon as possible and/or the WCS detecting that floor space in thewarehouse allocated for pallets is filled up or nearly filled up, theWCS may select the second candidate set of pallets instead of the firstcandidate set of pallets. On the other hand, if there is an urgentupcoming shipment for a type of item contained on the first candidateset of pallets, the WCS may select the first candidate set of palletsdespite the longer amount of time to condense, provided that the firstset of pallets will be condensed in time for the upcoming shipment.Other considerations for identifying and selecting pallets forcondensing are discussed later in this description.

With regard to warehouse 100 shown in FIG. 1, example candidate sets ofpallets for condensing may include any combination of pallet 126, pallet128, pallet 130, and pallet 132. For instance, the WCS may identifypallet 130 and pallet 132 as a candidate pair of pallets to condense,but may not ultimately select them to condense into a single palletbecause condensing them may result in a single pallet that exceeds itscapacity. Rather, the WCS may identify pallet 126 and pallet 130 ascandidates for condensing, and may then select them to condense. Otherexample combinations are possible as well.

Having identified pallets for condensing, the WCS may next determinewhat resources are available for performing the condensing (e.g.,retrieving the pallets, bringing them to a location, and condensing themat the location). In essence, the WCS may determine a cost function thatconsiders what else is going on in the warehouse and can thus identifyvarious information, such as which robotic devices are available toassist in the condensing, what time is best to perform the condensing,how long the condensing might take to perform, what the priority levelshould be for performing the condensing with respect to other scheduledor ongoing tasks, etc.

To facilitate this, at block 304, the method involves receivingreal-time robotics information relating to a plurality of roboticdevices positioned in the warehouse. In some examples, the real-timerobotics information may include (i) real-time locations of theplurality of robotic devices as positioned in the warehouse, (ii)real-time task progress data for the plurality of robotic devices, (iii)a schedule for performance of the tasks, and (iv) time measurements forone or more of the plurality of robotic devices to perform the tasks.The tasks may include tasks that are in-progress and/or tasks that arescheduled to be completed.

A time measurement for performance of a given task can take variousforms. Within examples, a time measurement for a given task may be fixedor known. For instance, unless an unforeseen issue arises (e.g., anobstacle blocks a robotic device's path, or a robotic device isdamaged), the WCS will expect that the task will take an amount of time(e.g., the task of bringing a pallet from one end of the warehouse tothe other will take five minutes). Within other examples, a timemeasurement for a given task may be variable to account for changes tothe task (e.g., if an unforeseen issue arises, or if a trajectory needsto be adjusted), and thus the WCS or other entity may periodically orcontinuously update the variable time measurement based on the progressof the task. Other examples are possible as well.

Within examples, the real-time robotics information may also includedefault trajectories or adjusted trajectories for each of the roboticdevices involved in a task or to-be-involved in a task. In this manner,the WCS may maintain or have access to a virtual map that indicateswhere in the warehouse the robotic devices trajectories will take themduring the tasks.

Within additional examples, the real-time robotics information may alsoinclude the type of robotic device (e.g., forklift, multi jointed arm,whether the robotic device is static or mobile), and may further includethe types of components that each robotic device has. For instance, theinformation may identify the type of mechanism the robotic device hasfor transporting items/pallets, such as a gripper (e.g., magnetic,fingers). Factors such as the robotic device type can identify aspectsthat may help or hinder the robotic device's usefulness in condensingsets of pallets, and may thus either lengthen or shorten the totalamount of time taken to condense the sets of pallets.

Within additional examples, the real-time robotics information may alsoinclude the battery levels of each robotic device, as the remainingbattery level of a given robotic device may factor into the WCS'sdecision of whether to select the given robotic device for theperformance of a task and/or the WCS's decision of how the given roboticdevice will assist in the performance of the task.

Generally, the task progress data may include data interpretable by theWCS as an indication of a current state of robotic devices in thewarehouse, including one or more robotic devices involved in aperformance of a task, or performance of a phase of a task when a taskinvolves multiple task phases. Within examples, the task progress datamay include a time at which a task or task phase was completed, and therobotic device that completed the task or task phase may transmit thetask progress data to the WCS at the completion time or within apredetermined time window after the completion time. The task progressdata may also indicate where the robotic device is/was when the task ortask phase is/was completed, and may also indicate where other roboticdevices and objects (e.g., other robotic devices, items, pallets, etc.that were involved in a performance of the task or task phase) arelocated in the warehouse and/or indicate a predicted future locationrepresentative of where the other robotic devices, items, pallets, etc.are travelling towards in the warehouse. Further, the task progress datamay indicate a current configuration of a given robotic device, such asa set of joint variables of the given robotic device, a configuration ofone or more robotic appendages of the given robotic device, and aheading of the given robotic device. Still further, the task progressdata may indicate a current status of one or more robotic devices, suchas a battery power level of a given robotic device or other diagnosticinformation, and/or information indicating a robotic appendage of thegiven robotic device that is not functioning correctly. It should beunderstood that information which may be indicated by the task progressdata, including the information described above, may alternatively betransmitted to the WCS at another time separate from the transmission ofthe task progress data.

Within examples, to facilitate performance of tasks throughout thewarehouse, including a task of condensing sets of pallets, the WCS mayperiodically determine a prioritization of scheduled tasks and/or aprioritization of ongoing tasks. For instance, the WCS may assign toeach task a priority level based on various considerations such as thosediscussed above (e.g., business goals, received orders). For instance,the task of condensing of pallets may have a lower priority level thanthe priority levels of tasks associated with fulfilling orders. Suchpriority levels can be adjusted based on changing conditions. Forexample, the WCS may cancel the condensing of pallets in the middle ofthe condensing process (or de-prioritize the condensing if the task wasscheduled but has not yet occurred) if the WCS determines that resourcesare needed for performing a different, more important/urgent task. Asanother example, if the WCS expects a large order for items, it maybecome more urgent to clear more space in the warehouse for the largerorder of items to be placed, and thus the WCS may assign a higherpriority (e.g., highest priority) to the task of condensing palletsthroughout the warehouse. Other examples are possible as well.

At block 306, the method involves, based on the real-time iteminformation and the real-time robotics information, determining anamount of time to condense the items on the set of pallets into a singlepallet by the plurality of robotic devices.

At block 308, the method involves, based on the real-time iteminformation and the real-time robotics information, determining aquantity of pallets that will become empty as a result of condensing theitems on the set of pallets into the single pallet.

At block 310, the method involves causing one or more of the pluralityof robotic devices to condense the items on the set of pallets into thesingle pallet based on the amount of time being less than a thresholdamount of time and further based on the quantity of pallets that willbecome empty being greater than a threshold quantity of pallets.

In some implementations, before determining how long the task ofcondensing the set of pallets will take, the WCS may use the real-timerobotics information to review ongoing and scheduled tasks in thewarehouse and determine robotic device availability for the task ofcondensing the set of pallets based on that review, thus determiningwhich robotic devices can fit at least a portion of the task ofcondensing into their schedule. As an example, in line with thediscussion above, once a robotic device completes a task, it may notifythe WCS that it has completed the task, and may then engage in anotherscheduled task for which it will travel along a trajectory to anotherside of the warehouse. The WCS may determine that there is sufficienttime in order for the robotic device to assist with the condensing alongits way to the other side of the warehouse, such as by retrieving apallet proximate to the trajectory and bringing it to a condensingstation. Other examples are possible as well.

The determined amount of time may include the respective amounts of timefor the robotic devices to perform one or more of the following actions:(i) retrieving the identified pallets from their current locations, (ii)bringing the identified pallets to a location for condensing, (iii)condensing the identified pallets into a single pallet, and/or (iv) andbringing the single pallet to a new location (e.g., loading the singlepallet onto a truck or placing the single pallet in storage). Thedetermined amount of time may include the time for performing otheractions as well.

Further, the determined amount of time to condense the set of palletsmay be exact (provided that the WCS has access to more precise roboticdevice task progress, location, and availability, for instance) or maybe an estimate. If other activities are ongoing in the warehouse thatcould possibly impact the time taken to condense the set of pallets, theWCS may not be able to determine an exact time. However, if many roboticdevices are not currently engaged in tasks or will not be engaged intasks over a period of time (e.g., overnight), the WCS may be able todetermine an exact time.

The WCS may perform the acts of identifying a set of pallets to condenseand/or causing the robotic devices condense the set of pallets based onone or more of a variety of conditions being met. As noted above, onesuch condition is whether a threshold quantity of pallets in thewarehouse will become empty as a result of the condensing, as notedabove. Within examples, the WCS may not instruct the robotic devices tocondense the set of pallets unless at least two pallets will becomeempty as a result of the condensing. Within additional examples, the WCSmay view this condition on a larger scale, as there may be scenarios inwhich it is desirable to create much more space on the floor, in palletracks, or in pallet stacks of the warehouse. For instance, the WCS maynot instruct the robotic devices to condense multiple different sets ofpallets into respective single pallets unless a larger number of palletswill become empty (e.g., fifty pallets).

As another example condition, the WCS may look to whether a high numberof the type of item is expected to be shipped soon. To facilitate this,as noted above, the WCS may determine an item shipment expectation anduse the item shipment expectation to determine a quantity of the type ofitem expected to be shipped. If the determined quantity is above athreshold shipment quantity, the WCS may instruct the robotic devices tocondense the set of pallets into a single pallet. In this manner, theWCS may be aware of and/or anticipate shipments of items for which itmay be desirable to have pallets at or near capacity of an item. Forinstance, in scenarios where there are not as many at-capacity palletsof the item as desired for the shipment, the WCS can instruct roboticdevices to gather various pallets of the item to condense in preparationfor the shipment.

Similarly, the WCS may use the item shipment expectation to determinethat an upcoming shipment requires a predetermined quantity of the typeof item on a single pallet. In other words, the WCS may determine that,on any given pallet for the upcoming shipment, there cannot be less thana certain quantity of items, and thus pallets should be condensed sothat each shipped pallet has at least that certain quantity of items. Ifthe WCS makes this determination, the WCS may responsively then instructthe robotic devices to condense the set of pallets.

As another example condition, the WCS may use the item shipmentexpectation to determine how much storage capacity (i.e., storage space)would be required for items that the WCS expects to be received at thewarehouse. If the determined storage space is above a threshold storagecapacity, the WCS may instruct the robotic devices to condense the setof pallets. Such a threshold storage capacity may represent the amountof free space currently in the warehouse, and thus if the determinedstorage space is above that threshold, the WCS may decide to make moreroom in the warehouse. For instance, given an example threshold of twothousand square feet, the WCS may be aware of or predict that there isan upcoming delivery of one thousand of a type of item and, based onthat number, determine an amount of storage space that would be neededto accommodate pallets of the one thousand items (e.g., five hundredpallet rack positions, three thousand square feet, or other manners ofquantifying required storage space). The WCS may then instruct therobotic devices to condense pallets because the required storage spaceexceeds the threshold of two thousand square feet.

Similarly, the WCS may use the pallet locations indicated by thereal-time item information as well as other information such as palletdimensions of the pallets in the warehouse to determine how much spaceis taken up in the warehouse by the identified set of pallets. If theamount of space is greater than a predetermined threshold amount ofspace, the WCS may instruct the robotic devices to condense the set ofpallets. The threshold amount of space which may vary depending on thedimensions of the pallets of the set.

As another example condition, the WCS may not condense the set ofpallets unless some or all of the pallets in the set of pallets arewithin a threshold distance from a shipping dock in the warehouse (e.g.,shipping dock 110).

As another example condition, the WCS may not instruct the roboticdevices to condense the set of pallets if there is not much time untilthe set of pallets are due to be shipped out, emptied, or otherwise nolonger present a disadvantage to the warehouse. In other words, the WCSmay not instruct the robotic devices to condense the set of pallets ifthe space taken up by some or all of the set of pallets would becomeavailable sooner (as a result of depleting the pallets naturally) thanif the robotic devices condensed the set of pallets. For instance, theWCS may use any records/histories, scheduled task information, or otherinformation discussed above to estimate an amount of time until the setof pallets will be depleted a result of warehouse activities other thancondensing items (e.g., loading full or less-than-full pallets ontotrucks for shipping). If the estimated amount of time is greater thanhow long it would take for the robotic devices to complete thecondensing of the set of pallets, then the WCS may decide to instructthe robotic devices to condense the set of pallets. But if it wouldlikely take the robotic devices longer to condense the set of palletsthan it would take for the set of pallets to no longer present adisadvantage to the warehouse, the WCS may instead decide to notinstruct the robotic devices to condense the set of pallets (or maycancel the condensing if the task of condensing is already underway). Inother implementations, the WCS may not compare the estimated amount oftime to the determined time for the robotic devices to condense the setof pallets, and may rather compare the estimated amount of time to athreshold. For example, if the time it would take for the set of palletsto no longer present a disadvantage to the warehouse is longer thantwenty-four hours, the WCS may decide to instruct the robotic devices tocondense the set of pallets. Other examples are possible as well.

Similarly, if the WCS determines that the set of pallets will bedepleted soon as a result of other warehouse activities (e.g.,activities other than condensing pallets), the benefit to condensing theset of pallets may be to gain additional storage space in the warehouseearlier by condensing the set of pallets before the set of pallets wouldhave otherwise been depleted. As such, the WCS may weigh this optionagainst the option of merely waiting for the set of pallets to bedepleted by other warehouse activities. In particular, the WCS maydetermine whether the value of waiting for the set of pallets to bedepleted by other warehouse activities outweighs the value provided bycondensing the set of pallets and gaining additional storage space forthe amount of time between the time condensing ends and the time the setof pallets would have been depleted by other warehouse activities.

In some implementations, the WCS may choose one or more robotic devicesto take part in the task of condensing if the robotic device(s) is/arewithin a threshold distance from a condensing station and/or within athreshold distance from the locations of the identified set of pallets,thereby potentially reducing the amount of time it might take for thechosen robotic device(s) to bring the set of pallets to the condensingstation and in turn reducing the total amount of time for the task ofcondensing the set of pallets. The WCS may then instruct the chosenrobotic device(s) to retrieve the set of pallets and bring them to thecondensing station.

The condensing station may be designated and fixed in the warehouse(e.g., condensing station 134) or may be determined on the fly, suchthat certain areas of the warehouse can be used as somewhat-impromptu,makeshift condensing station that is optimal for the given situation. Ifthe condensing station is fixed, the WCS may receive or otherwise accessinformation indicating the location of the condensing station (e.g., amap of the warehouse) to facilitate instruction to the robotic devicesto condense the set of pallets. On the other hand, the WCS may use thereal-time item information and the real-time robotics information todetermine an optimal condensing station location in the warehouse atwhich to condense the set of pallets, such as a location that is (i)within a threshold distance from a shipping dock, (ii) within athreshold distance from the locations of set of pallets, and/or (iii)within a threshold distance from the trajectories of robotic devicesthat might be available to assist with the condensing before, after, orduring performance of another task of theirs. The WCS may then providethe location of the optimal condensing station to the robotic devices aspart of the instructions to condense the set of pallets. In someinstances, the WCS may first schedule a time interval for the condensingof the set of pallets and then determine the optimal condensing stationlocation to be within a threshold distance from one or more of therespective trajectories along which one or more robotic devices will betravelling before the scheduled time interval.

In some implementations, the WCS may consider the real-time roboticsinformation and/or other scheduled warehouse activities to determine atime interval during which to condense pallets in the warehouse, and maythus instruct the robotic devices to condense pallets during the timeinterval. For example, the WCS may determine that it will take therobotic devices one hour to condense one or more identified sets ofpallets and may identify a one hour window in the following twenty fourhours during which to condense the set(s) of pallets.

In similar implementations, the WCS may consider the real-time roboticsinformation and/or other scheduled warehouse activities to allocate aninterval of time for a recurring condensing of pallets. At least some ofthe operations discussed herein may then be performed on a periodicbasis at the allocated time interval. For instance, if less warehouseactivities occur overnight during a seven hour period of time, the WCSmay determine that the WCS should take actions to identify potentialsets of pallets to condense, and take those actions every night duringthat seven hour period, every other night during that period, or everySunday during that period.

FIGS. 4A, 4B, and 4C illustrate example operation of robotic devices ina warehouse, in accordance with at least some implementations describedherein. In particular, FIGS. 4A, 4B, and 4C illustrate moments in timeduring a condensing process.

As shown in FIG. 4A, there are two pallets, pallet 400 and pallet 402,that may together make up an identified set of pallets or may be twomembers of a larger set of pallets. FIG. 4A also shows a condensingstation 404 and a pedestal robot 406, such as the pedestal robot 112described with regard to FIG. 1, which may be configured to assist withthe process of condensing items on pallets into single pallets, andwhich the WCS may instruct to assist with the task. Further, FIG. 4Ashows two autonomous fork trucks, fork truck 408 and fork truck 410,that the WCS has instructed to assist with the task of condensing pallet400 and pallet 402. As such, fork truck 408 may travel along dotted path412 to retrieve pallet 400, and then travel along dotted path 414 todeliver pallet 400 to condensing station 404. In addition, fork truck410 may travel along dashed path 416 to retrieve pallet 402, and thentravel along dashed path 418 to deliver pallet 402 to condensing station404. FIG. 4B shows both pallet 400 and pallet 402 at condensing station404. In practice, the WCS may instruct pedestal robot 406 to condensepallet 400 and pallet 402 into single pallet 420 shown in FIG. 4C.Further, as shown in FIG. 4C, the WCS may instruct fork truck 408 totravel along dashed path 422 to deliver pallet 420 to a predeterminedlocation 424, and at some point may also instruct fork truck 410 toleave condensing station 404 resume a current task or begin a new task.Other example illustrations are possible as well.

It should be understood that arrangements described herein are forpurposes of example only. As such, those skilled in the art willappreciate that other arrangements and other elements (e.g. machines,interfaces, operations, orders, and groupings of operations, etc.) canbe used instead, and some elements may be omitted altogether accordingto the desired results. Further, many of the elements that are describedare operational entities that may be implemented as discrete ordistributed components or in conjunction with other components, in anysuitable combination and location, or other structural elementsdescribed as independent structures may be combined.

While various aspects and implementations have been disclosed herein,other aspects and implementations will be apparent to those skilled inthe art. The various aspects and implementations disclosed herein arefor purposes of illustration and are not intended to be limiting, withthe true scope being indicated by the following claims, along with thefull scope of equivalents to which such claims are entitled. It is alsoto be understood that the terminology used herein is for the purpose ofdescribing particular implementations only, and is not intended to belimiting.

What is claimed is:
 1. A method comprising: receiving, at a warehouse control system (WCS), real-time item information including real-time locations of pallets positioned in a warehouse and real-time inventory of items arranged on the pallets, wherein the real-time inventory of the items includes a content of each pallet; based on the real-time item information, identifying a set of two or more pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving, at the WCS, real-time robotics information relating to a plurality of robotic devices positioned in the warehouse; based on the real-time item information and the real-time robotics information, determining an amount of time to condense the items on the set of pallets into a single pallet by the plurality of robotic devices; based on the real-time item information and the real-time robotics information, determining a quantity of pallets that will become empty as a result of condensing the items on the set of pallets into the single pallet; and causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet based on the amount of time being less than a threshold amount of time and further based on the quantity of pallets that will become empty being greater than a threshold quantity of pallets.
 2. The method of claim 1, further comprising: based on at least one of receiving records and order fulfillment and shipping records, determining an item shipment expectation including a new item expected to be received at the warehouse at a future date and an item present at the warehouse marked for delivery at the future date; and based on the item shipment expectation, determining a quantity of the type of item expected to be shipped, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the quantity of the type of item expected to be shipped being greater than a threshold shipment quantity.
 3. The method of claim 1, further comprising: based on at least one of receiving records and order fulfillment and shipping records, determining an item shipment expectation including a new item expected to be received at the warehouse at a future date and an item present at the warehouse marked for delivery at the future dates; and based on the item shipment expectation, determining an amount of storage capacity required for items expected to be received at the warehouse, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the amount of storage capacity required for items expected to be received at the warehouse being greater than a threshold storage capacity.
 4. The method of claim 1, further comprising: based on at least one of receiving records and order fulfillment and shipping records, making a determination that an upcoming shipment requires a predetermined quantity of the type of item on the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the determination that the upcoming shipment requires the predetermined quantity of the type of item on a single pallet.
 5. The method of claim 1, further comprising: based on the real-time item information, determining an amount of space taken up in the warehouse by the set of pallets, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the amount of space taken up in the warehouse by the set of pallets being greater than a threshold amount of space.
 6. The method of claim 1, wherein the threshold amount of time is a first threshold amount of time, the method further comprising: estimating an amount of time until the set of pallets become empty as a result of warehouse activities other than condensing the items on the set of pallets into the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the estimated amount of time until the set of pallets become empty being greater than a second threshold amount of time.
 7. The method of claim 1, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the set of pallets being within a threshold distance from a shipping dock in the warehouse.
 8. The method of claim 1, further comprising: estimating an amount of time until the set of pallets become empty as a result of warehouse activities other than condensing the items on the set of pallets into the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the amount of time being less than the estimated amount of time until the set of pallets become empty.
 9. A system comprising: a plurality of robotic devices in a warehouse; at least one processor; and data storage comprising instructions executable by the at least one processor to cause the system to perform operations comprising: receiving real-time item information including real-time locations of pallets positioned in the warehouse and real-time inventory of items arranged on the pallets, wherein the real-time inventory of the items includes a content of each pallet; based on the real-time item information, identifying a set of two or more pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information relating to the plurality of robotic devices; based on the real-time item information and the real-time robotics information, determining an amount of time to condense the items on the set of pallets into a single pallet by the plurality of robotic devices; based on the real-time item information and the real-time robotics information, determining a quantity of pallets that will become empty as a result of condensing the items on the set of pallets into the single pallet; and causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet based on the amount of time being less than a threshold amount of time and further based on the quantity of pallets that will become empty being greater than a threshold quantity of pallets.
 10. The system of claim 9, the operations further comprising: receiving, by the WCS, a predetermined condensing station location in the warehouse; and selecting, from the plurality of robotic devices, one or more robotic devices to relocate the set of pallets to the predetermined condensing station location based on the selected one or more robotic devices being within a threshold distance from the predetermined condensing station, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet comprises instructing the selected one or more robotic devices of the plurality to relocate the set of pallets to the predetermined condensing station location and condense the items on the set of pallets into the single pallet at the predetermined condensing station location.
 11. The system of claim 9, the operations further comprising: based on the real-time item information and the real-time robotics information, determining an optimal condensing station location in the warehouse at which to condense the items on the set of pallets into the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet comprises instructing one or more robotic devices of the plurality to relocate the set of pallets to the optimal condensing station location and condense the items on the set of pallets into the single pallet at the optimal condensing station location.
 12. The system of claim 11, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the optimal condensing station location being within a threshold distance from a shipping dock in the warehouse.
 13. The system of claim 11, further comprising: based on the real-time robotics information and scheduled warehouse activities, determining a time interval at a future date during which to condense the items on the set of pallets into the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the optimal condensing station location being within a threshold distance from predetermined paths along which the one or more robotic devices will be travelling before the time interval while performing respective tasks.
 14. The system of claim 9, the operations further comprising: based on the real-time robotics information and other scheduled warehouse activities, allocating a time interval for recurring condensing of items on pallets to single pallets, wherein at least a portion of the method is periodically performed at the allocated time interval to condense items on pallets to single pallets.
 15. The system of claim 9, the operations further comprising: based on the real-time robotics information and scheduled warehouse activities, determining a time interval during which to condense items on pallets, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet comprises causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet during the determined time interval.
 16. A non-transitory computer-readable medium having stored thereon program instructions that when executed by a computing system that includes at least one processor cause the computing system to perform operations comprising: receiving real-time item information including real-time locations of pallets positioned in a warehouse and real-time inventory of items arranged on the pallets, wherein the real-time inventory of the items includes a content of each pallet; based on the real-time item information, identifying a set of two or more pallets of which at least one pallet includes less than a threshold quantity of a type of item; receiving real-time robotics information relating to a plurality of robotic devices positioned in the warehouse; based on the real-time item information and the real-time robotics information, determining an amount of time to condense the items on the set of pallets into a single pallet by the plurality of robotic devices; based on the real-time item information and the real-time robotics information, determining a quantity of pallets that will become empty as a result of condensing the items on the set of pallets into the single pallet; and causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet based on the amount of time being less than a threshold amount of time and further based on the quantity of pallets that will become empty being greater than a threshold quantity of pallets.
 17. The non-transitory computer-readable medium of claim 16, wherein the real-time item information further includes expiration dates of the items and lot numbers of the items, and wherein identifying the set of pallets of which at least one pallet includes less than the threshold quantity of the type of item based on the real-time item information comprises, based on the real-time item information, identifying the set of pallets (i) of which at least one pallet includes less than the threshold quantity of the type of item and (ii) that each include items of the type that have one or both of: the same expiration date and the same lot number.
 18. The non-transitory computer-readable medium of claim 16, the operations further comprising: based on at least one of receiving records and order fulfillment and shipping records, making a determination that an upcoming shipment requires a predetermined quantity of the type of item on the single pallet, wherein causing one or more of the plurality of robotic devices to condense the items on the set of pallets into the single pallet is further based on the determination that the upcoming shipment requires the predetermined quantity of the type of item on a single pallet.
 19. The non-transitory computer-readable medium of claim 16, wherein identifying the set of two or more pallets of which at least one pallet includes less than the threshold quantity of the type of item based on the real-time item information comprises, based on the real-time item information, identifying the set of two or more pallets (i) of which at least one pallet includes less than the threshold quantity of the type of item and (ii) of which at least one other pallet includes less than a predetermined maximum capacity of items.
 20. The non-transitory computer-readable medium of claim 16, wherein the real-time robotics information includes (i) real-time locations of the plurality of robotic devices as positioned in the warehouse, (ii) real-time task progress data for the plurality of robotic devices, (iii) a schedule for performance of the tasks, and (iv) time measurements for one or more of the plurality of robotic devices to perform the tasks. 